Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows76
Duplicate rows (%)0.6%
Total size in memory1.4 MiB
Average record size in memory122.0 B

Variable types

Numeric15
Categorical3

Alerts

Dataset has 76 (0.6%) duplicate rowsDuplicates
Administrative is highly overall correlated with Administrative_DurationHigh correlation
Administrative_Duration is highly overall correlated with AdministrativeHigh correlation
Informational is highly overall correlated with Informational_DurationHigh correlation
Informational_Duration is highly overall correlated with InformationalHigh correlation
ProductRelated is highly overall correlated with ProductRelated_Duration and 1 other fieldsHigh correlation
ProductRelated_Duration is highly overall correlated with ProductRelatedHigh correlation
BounceRates in % is highly overall correlated with ExitRates in %High correlation
ExitRates in % is highly overall correlated with ProductRelated and 1 other fieldsHigh correlation
VisitorType is highly imbalanced (59.9%)Imbalance
Administrative has 5768 (46.8%) zerosZeros
Administrative_Duration has 5903 (47.9%) zerosZeros
Informational has 9699 (78.7%) zerosZeros
Informational_Duration has 9925 (80.5%) zerosZeros
ProductRelated_Duration has 755 (6.1%) zerosZeros
BounceRates in % has 5518 (44.8%) zerosZeros
PageValues has 9600 (77.9%) zerosZeros
SpecialDay (probability) has 11079 (89.9%) zerosZeros
Month has 433 (3.5%) zerosZeros

Reproduction

Analysis started2023-02-13 10:40:45.410627
Analysis finished2023-02-13 10:41:17.634123
Duration32.22 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Administrative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3151663
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:17.732556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3217841
Coefficient of variation (CV)1.4347929
Kurtosis4.7011462
Mean2.3151663
Median Absolute Deviation (MAD)1
Skewness1.9603572
Sum28546
Variance11.03425
MonotonicityNot monotonic
2023-02-13T16:11:17.862725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
Other values (17) 557
 
4.5%
ValueCountFrequency (%)
0 5768
46.8%
1 1354
 
11.0%
2 1114
 
9.0%
3 915
 
7.4%
4 765
 
6.2%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.7%
8 287
 
2.3%
9 225
 
1.8%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 4
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
 
< 0.1%
18 12
0.1%
17 16
0.1%

Administrative_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3335
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.818611
Minimum0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:17.996896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.26637
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.77911
Coefficient of variation (CV)2.1873564
Kurtosis50.556739
Mean80.818611
Median Absolute Deviation (MAD)7.5
Skewness5.615719
Sum996493.47
Variance31250.853
MonotonicityNot monotonic
2023-02-13T16:11:18.138492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5903
47.9%
4 56
 
0.5%
5 53
 
0.4%
7 45
 
0.4%
11 42
 
0.3%
6 41
 
0.3%
14 37
 
0.3%
9 35
 
0.3%
15 33
 
0.3%
10 32
 
0.3%
Other values (3325) 6053
49.1%
ValueCountFrequency (%)
0 5903
47.9%
1.333333333 1
 
< 0.1%
2 15
 
0.1%
3 26
 
0.2%
3.5 4
 
< 0.1%
4 56
 
0.5%
4.333333333 1
 
< 0.1%
4.5 2
 
< 0.1%
4.75 1
 
< 0.1%
5 53
 
0.4%
ValueCountFrequency (%)
3398.75 1
< 0.1%
2720.5 1
< 0.1%
2657.318056 1
< 0.1%
2629.253968 1
< 0.1%
2407.42381 1
< 0.1%
2156.166667 1
< 0.1%
2137.112745 1
< 0.1%
2086.75 1
< 0.1%
2047.234848 1
< 0.1%
1951.279141 1
< 0.1%

Informational
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50356853
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:18.266143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2701564
Coefficient of variation (CV)2.522311
Kurtosis26.932266
Mean0.50356853
Median Absolute Deviation (MAD)0
Skewness4.0364638
Sum6209
Variance1.6132973
MonotonicityNot monotonic
2023-02-13T16:11:18.662914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
9 15
 
0.1%
8 14
 
0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9699
78.7%
1 1041
 
8.4%
2 728
 
5.9%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
8 14
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 7
 
0.1%
9 15
0.1%
8 14
 
0.1%
7 36
0.3%

Informational_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1258
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.472398
Minimum0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:18.789793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.74929
Coefficient of variation (CV)4.0829563
Kurtosis76.316853
Mean34.472398
Median Absolute Deviation (MAD)0
Skewness7.5791847
Sum425044.67
Variance19810.364
MonotonicityNot monotonic
2023-02-13T16:11:18.944654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9925
80.5%
9 33
 
0.3%
7 26
 
0.2%
10 26
 
0.2%
6 26
 
0.2%
12 23
 
0.2%
13 23
 
0.2%
16 22
 
0.2%
8 22
 
0.2%
11 21
 
0.2%
Other values (1248) 2183
 
17.7%
ValueCountFrequency (%)
0 9925
80.5%
1 3
 
< 0.1%
1.5 1
 
< 0.1%
2 11
 
0.1%
2.5 1
 
< 0.1%
3 16
 
0.1%
3.5 1
 
< 0.1%
4 17
 
0.1%
5 18
 
0.1%
5.5 3
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2195.3 1
< 0.1%
2166.5 1
< 0.1%
2050.433333 1
< 0.1%
1949.166667 1
< 0.1%
1830.5 1
< 0.1%
1779.166667 1
< 0.1%
1778 1
< 0.1%

ProductRelated
Real number (ℝ)

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.731468
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:19.091702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.475503
Coefficient of variation (CV)1.4016214
Kurtosis31.211707
Mean31.731468
Median Absolute Deviation (MAD)13
Skewness4.3415164
Sum391249
Variance1978.0704
MonotonicityNot monotonic
2023-02-13T16:11:19.227161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 622
 
5.0%
2 465
 
3.8%
3 458
 
3.7%
4 404
 
3.3%
6 396
 
3.2%
7 391
 
3.2%
5 382
 
3.1%
8 370
 
3.0%
10 330
 
2.7%
9 317
 
2.6%
Other values (301) 8195
66.5%
ValueCountFrequency (%)
0 38
 
0.3%
1 622
5.0%
2 465
3.8%
3 458
3.7%
4 404
3.3%
5 382
3.1%
6 396
3.2%
7 391
3.2%
8 370
3.0%
9 317
2.6%
ValueCountFrequency (%)
705 1
< 0.1%
686 1
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%
449 1
< 0.1%

ProductRelated_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9551
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.7462
Minimum0
Maximum63973.522
Zeros755
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:19.383436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369
Q31464.1572
95-th percentile4300.2891
Maximum63973.522
Range63973.522
Interquartile range (IQR)1280.0197

Descriptive statistics

Standard deviation1913.6693
Coefficient of variation (CV)1.6017371
Kurtosis137.17416
Mean1194.7462
Median Absolute Deviation (MAD)500.9369
Skewness7.2632277
Sum14731221
Variance3662130.1
MonotonicityNot monotonic
2023-02-13T16:11:19.539789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 755
 
6.1%
17 21
 
0.2%
11 17
 
0.1%
8 17
 
0.1%
15 16
 
0.1%
12 15
 
0.1%
19 15
 
0.1%
22 15
 
0.1%
13 14
 
0.1%
7 14
 
0.1%
Other values (9541) 11431
92.7%
ValueCountFrequency (%)
0 755
6.1%
0.5 1
 
< 0.1%
1 2
 
< 0.1%
2.333333333 1
 
< 0.1%
2.666666667 1
 
< 0.1%
3 5
 
< 0.1%
4 10
 
0.1%
5 13
 
0.1%
5.333333333 1
 
< 0.1%
6 5
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
29970.46597 1
< 0.1%
27009.85943 1
< 0.1%
24844.1562 1
< 0.1%
23888.81 1
< 0.1%
23342.08205 1
< 0.1%
23050.10414 1
< 0.1%
21857.04648 1
< 0.1%
21672.24425 1
< 0.1%

BounceRates in %
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1872
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138
Minimum0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:19.692823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.016812558
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.016812558

Descriptive statistics

Standard deviation0.048488322
Coefficient of variation (CV)2.185007
Kurtosis7.7231594
Mean0.02219138
Median Absolute Deviation (MAD)0.0031124675
Skewness2.9478553
Sum273.61972
Variance0.0023511174
MonotonicityNot monotonic
2023-02-13T16:11:19.849245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5518
44.8%
0.2 700
 
5.7%
0.066666667 134
 
1.1%
0.028571429 115
 
0.9%
0.05 113
 
0.9%
0.033333333 101
 
0.8%
0.025 100
 
0.8%
0.016666667 99
 
0.8%
0.1 98
 
0.8%
0.04 96
 
0.8%
Other values (1862) 5256
42.6%
ValueCountFrequency (%)
0 5518
44.8%
2.73 × 10-51
 
< 0.1%
3.35 × 10-51
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.2 700
5.7%
0.183333333 1
 
< 0.1%
0.18 5
 
< 0.1%
0.176923077 1
 
< 0.1%
0.175 1
 
< 0.1%
0.166666667 4
 
< 0.1%
0.164285714 1
 
< 0.1%
0.164230769 1
 
< 0.1%
0.161904762 1
 
< 0.1%
0.16 3
 
< 0.1%

ExitRates in %
Real number (ℝ)

Distinct4777
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.043072798
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:20.013806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.025156403
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.048596541
Coefficient of variation (CV)1.128242
Kurtosis4.0170346
Mean0.043072798
Median Absolute Deviation (MAD)0.01417258
Skewness2.148789
Sum531.0876
Variance0.0023616238
MonotonicityNot monotonic
2023-02-13T16:11:20.167926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 710
 
5.8%
0.1 338
 
2.7%
0.05 329
 
2.7%
0.033333333 291
 
2.4%
0.066666667 267
 
2.2%
0.025 224
 
1.8%
0.04 214
 
1.7%
0.016666667 181
 
1.5%
0.02 167
 
1.4%
0.022222222 152
 
1.2%
Other values (4767) 9457
76.7%
ValueCountFrequency (%)
0 76
0.6%
0.000175593 1
 
< 0.1%
0.000250438 1
 
< 0.1%
0.000262123 1
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 1
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 710
5.8%
0.192307692 1
 
< 0.1%
0.188888889 2
 
< 0.1%
0.186666667 4
 
< 0.1%
0.183333333 2
 
< 0.1%
0.181818182 1
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 3
 
< 0.1%
0.177777778 5
 
< 0.1%
0.175 6
 
< 0.1%

PageValues
Real number (ℝ)

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8892579
Minimum0
Maximum361.76374
Zeros9600
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:20.331289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.160528
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.568437
Coefficient of variation (CV)3.1529332
Kurtosis65.635694
Mean5.8892579
Median Absolute Deviation (MAD)0
Skewness6.3829642
Sum72614.549
Variance344.78684
MonotonicityNot monotonic
2023-02-13T16:11:20.474008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9600
77.9%
53.988 6
 
< 0.1%
42.29306752 3
 
< 0.1%
59.988 2
 
< 0.1%
16.1585582 2
 
< 0.1%
44.89345937 2
 
< 0.1%
14.1273698 2
 
< 0.1%
34.03997536 2
 
< 0.1%
10.99901844 2
 
< 0.1%
58.9241766 2
 
< 0.1%
Other values (2694) 2707
 
22.0%
ValueCountFrequency (%)
0 9600
77.9%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.131837013 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
360.9533839 1
< 0.1%
287.9537928 1
< 0.1%
270.7846931 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 1
< 0.1%
255.5691579 1
< 0.1%
254.6071579 1
< 0.1%
246.7585902 1
< 0.1%
239.98 1
< 0.1%

SpecialDay (probability)
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061427413
Minimum0
Maximum1
Zeros11079
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:20.604243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19891727
Coefficient of variation (CV)3.2382492
Kurtosis9.9136589
Mean0.061427413
Median Absolute Deviation (MAD)0
Skewness3.3026667
Sum757.4
Variance0.039568082
MonotonicityNot monotonic
2023-02-13T16:11:20.701218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11079
89.9%
0.6 351
 
2.8%
0.8 325
 
2.6%
0.4 243
 
2.0%
0.2 178
 
1.4%
1 154
 
1.2%
ValueCountFrequency (%)
0 11079
89.9%
0.2 178
 
1.4%
0.4 243
 
2.0%
0.6 351
 
2.8%
0.8 325
 
2.6%
1 154
 
1.2%
ValueCountFrequency (%)
1 154
 
1.2%
0.8 325
 
2.6%
0.6 351
 
2.8%
0.4 243
 
2.0%
0.2 178
 
1.4%
0 11079
89.9%

Month
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1639903
Minimum0
Maximum9
Zeros433
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-02-13T16:11:20.802567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median6
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3701994
Coefficient of variation (CV)0.45898603
Kurtosis-0.36832961
Mean5.1639903
Median Absolute Deviation (MAD)1
Skewness-0.83253451
Sum63672
Variance5.6178451
MonotonicityNot monotonic
2023-02-13T16:11:20.900191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 3364
27.3%
7 2998
24.3%
5 1907
15.5%
1 1727
14.0%
8 549
 
4.5%
9 448
 
3.6%
0 433
 
3.5%
3 432
 
3.5%
4 288
 
2.3%
2 184
 
1.5%
ValueCountFrequency (%)
0 433
 
3.5%
1 1727
14.0%
2 184
 
1.5%
3 432
 
3.5%
4 288
 
2.3%
5 1907
15.5%
6 3364
27.3%
7 2998
24.3%
8 549
 
4.5%
9 448
 
3.6%
ValueCountFrequency (%)
9 448
 
3.6%
8 549
 
4.5%
7 2998
24.3%
6 3364
27.3%
5 1907
15.5%
4 288
 
2.3%
3 432
 
3.5%
2 184
 
1.5%
1 1727
14.0%
0 433
 
3.5%

OperatingSystems
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1240065
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:20.997534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91132483
Coefficient of variation (CV)0.42905934
Kurtosis10.456843
Mean2.1240065
Median Absolute Deviation (MAD)0
Skewness2.066285
Sum26189
Variance0.83051294
MonotonicityNot monotonic
2023-02-13T16:11:21.094077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6601
53.5%
1 2585
 
21.0%
3 2555
 
20.7%
4 478
 
3.9%
8 79
 
0.6%
6 19
 
0.2%
7 7
 
0.1%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 2585
 
21.0%
2 6601
53.5%
3 2555
 
20.7%
4 478
 
3.9%
5 6
 
< 0.1%
6 19
 
0.2%
7 7
 
0.1%
8 79
 
0.6%
ValueCountFrequency (%)
8 79
 
0.6%
7 7
 
0.1%
6 19
 
0.2%
5 6
 
< 0.1%
4 478
 
3.9%
3 2555
 
20.7%
2 6601
53.5%
1 2585
 
21.0%

Browser
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3570965
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:21.195217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7172767
Coefficient of variation (CV)0.72855594
Kurtosis12.746733
Mean2.3570965
Median Absolute Deviation (MAD)0
Skewness3.2423496
Sum29063
Variance2.9490392
MonotonicityNot monotonic
2023-02-13T16:11:21.302425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7961
64.6%
1 2462
 
20.0%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
10 163
 
1.3%
8 135
 
1.1%
3 105
 
0.9%
13 61
 
0.5%
7 49
 
0.4%
Other values (3) 17
 
0.1%
ValueCountFrequency (%)
1 2462
 
20.0%
2 7961
64.6%
3 105
 
0.9%
4 736
 
6.0%
5 467
 
3.8%
6 174
 
1.4%
7 49
 
0.4%
8 135
 
1.1%
9 1
 
< 0.1%
10 163
 
1.3%
ValueCountFrequency (%)
13 61
 
0.5%
12 10
 
0.1%
11 6
 
< 0.1%
10 163
 
1.3%
9 1
 
< 0.1%
8 135
 
1.1%
7 49
 
0.4%
6 174
 
1.4%
5 467
3.8%
4 736
6.0%

Region
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1473642
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:21.401254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4015912
Coefficient of variation (CV)0.76304842
Kurtosis-0.1486803
Mean3.1473642
Median Absolute Deviation (MAD)2
Skewness0.98354916
Sum38807
Variance5.7676405
MonotonicityNot monotonic
2023-02-13T16:11:21.514450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4780
38.8%
3 2403
19.5%
4 1182
 
9.6%
2 1136
 
9.2%
6 805
 
6.5%
7 761
 
6.2%
9 511
 
4.1%
8 434
 
3.5%
5 318
 
2.6%
ValueCountFrequency (%)
1 4780
38.8%
2 1136
 
9.2%
3 2403
19.5%
4 1182
 
9.6%
5 318
 
2.6%
6 805
 
6.5%
7 761
 
6.2%
8 434
 
3.5%
9 511
 
4.1%
ValueCountFrequency (%)
9 511
 
4.1%
8 434
 
3.5%
7 761
 
6.2%
6 805
 
6.5%
5 318
 
2.6%
4 1182
 
9.6%
3 2403
19.5%
2 1136
 
9.2%
1 4780
38.8%

TrafficType
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0695864
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size96.5 KiB
2023-02-13T16:11:21.627521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0251692
Coefficient of variation (CV)0.98908557
Kurtosis3.4797106
Mean4.0695864
Median Absolute Deviation (MAD)1
Skewness1.9629867
Sum50178
Variance16.201987
MonotonicityNot monotonic
2023-02-13T16:11:21.741329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 3913
31.7%
1 2451
19.9%
3 2052
16.6%
4 1069
 
8.7%
13 738
 
6.0%
10 450
 
3.6%
6 444
 
3.6%
8 343
 
2.8%
5 260
 
2.1%
11 247
 
2.0%
Other values (10) 363
 
2.9%
ValueCountFrequency (%)
1 2451
19.9%
2 3913
31.7%
3 2052
16.6%
4 1069
 
8.7%
5 260
 
2.1%
6 444
 
3.6%
7 40
 
0.3%
8 343
 
2.8%
9 42
 
0.3%
10 450
 
3.6%
ValueCountFrequency (%)
20 198
 
1.6%
19 17
 
0.1%
18 10
 
0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
15 38
 
0.3%
14 13
 
0.1%
13 738
6.0%
12 1
 
< 0.1%
11 247
 
2.0%

VisitorType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
2
10551 
0
1694 
1
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Length

2023-02-13T16:11:21.856847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T16:11:21.990499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10551
85.6%
0 1694
 
13.7%
1 85
 
0.7%

Weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
9462 
1
2868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Length

2023-02-13T16:11:22.095990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T16:11:22.193670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9462
76.7%
1 2868
 
23.3%

Revenue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
0
10422 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12330
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Length

2023-02-13T16:11:22.292637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T16:11:22.402598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10422
84.5%
1 1908
 
15.5%

Interactions

2023-02-13T16:11:15.244568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.035496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.013237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.942929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.874440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.777423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.910237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.902398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.095989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.301597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.224872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.275650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.131337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.430076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.304690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.377872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.282890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.150851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.188610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.988326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.900047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.037271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.038319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.231138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.417034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.344036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.394679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.265472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.547886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.438285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.515438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.399448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.287788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.305740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.115546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.034311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.179158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.174436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.375492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.547191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.473263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.512941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.403237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.676975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.578384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.647201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.520726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.403925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.418003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.234921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.163597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.304048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.304572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.501362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.666250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.604367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.626796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.531302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.790436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.702755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.770972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.638221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.531867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.536795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.368514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.285438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.433257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.439495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.631537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.788107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.740722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.750953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.898365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.913385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.828005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.902660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.761364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.660014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.658099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.497254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.418904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.565116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.582609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.766392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.916135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.934462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.871035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.025841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.040771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.954886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.045448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:47.883351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.788013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.781307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.631547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.550894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.698566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.724073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:01.900338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.037551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.099380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.999278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.153441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.168160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.089323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.167593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.007163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:49.925514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:51.909950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.768125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.687905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.839925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:59.869996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.043369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.180954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.259185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.140871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.290323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.310208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.237644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.302924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.145619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.063795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.048021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:53.907040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:55.834043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:57.978031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.031859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.379472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.325747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.388520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.273528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.421488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.439125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.378725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.430527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.264998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.203772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.169132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.037428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.129499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.111477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.239997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.511346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.456704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.519524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.398079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.555573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.565162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.513026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.551383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.387989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.330171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.286794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.156857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.263834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.228269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.406475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.639203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.589134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.640848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.524499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.714496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.689736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.627969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.662767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.505627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.447836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.391082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.269607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.381585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.354434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.536049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.762605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.710062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.758847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.625053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:10.850338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.805460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.739699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.785590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.635812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.576240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.508660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.395769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.519826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.496895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.674862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:02.897488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.831671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:06.888726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.759578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.026561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:12.934734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.864794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:16.897777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.755365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.684107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.624147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.514438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.646665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.619936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.815842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.012842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:04.956674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.012699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:08.871272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.158973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.050030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:14.977706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:17.022338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:48.883976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:50.817086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:52.751326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:54.646156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:56.775088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:10:58.763568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:00.956587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:03.160018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:05.088592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:07.145219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:09.003305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:11.289517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:13.176733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-13T16:11:15.100311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-13T16:11:22.506824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRates in %ExitRates in %PageValuesSpecialDay (probability)MonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
Administrative1.0000.9410.3690.3630.4600.422-0.155-0.4340.328-0.1250.083-0.005-0.0120.009-0.0120.0860.0280.131
Administrative_Duration0.9411.0000.3570.3520.4300.414-0.164-0.4380.317-0.1320.080-0.007-0.0230.019-0.0150.0070.0000.064
Informational0.3690.3571.0000.9510.3690.3680.006-0.1860.219-0.0540.0480.000-0.020-0.023-0.0290.0280.0110.078
Informational_Duration0.3630.3520.9511.0000.3610.363-0.002-0.2000.224-0.0540.0450.003-0.013-0.015-0.0260.0080.0000.068
ProductRelated0.4600.4300.3690.3611.0000.883-0.052-0.5190.342-0.0220.1140.0210.044-0.021-0.0700.0790.0000.127
ProductRelated_Duration0.4220.4140.3680.3630.8831.000-0.080-0.4770.360-0.0500.1040.0230.046-0.010-0.0730.0350.0040.072
BounceRates in %-0.155-0.1640.006-0.002-0.052-0.0801.0000.602-0.1240.1350.0050.053-0.047-0.0180.0160.1230.0500.170
ExitRates in %-0.434-0.438-0.186-0.200-0.519-0.4770.6021.000-0.3080.151-0.0630.022-0.016-0.0040.0220.1840.0650.245
PageValues0.3280.3170.2190.2240.3420.360-0.124-0.3081.000-0.0700.076-0.0120.0260.001-0.0180.1100.0310.413
SpecialDay (probability)-0.125-0.132-0.054-0.054-0.022-0.0500.1350.151-0.0701.0000.0210.0230.021-0.0150.1100.0640.2590.086
Month0.0830.0800.0480.0450.1140.1040.005-0.0630.0760.0211.000-0.003-0.014-0.0270.0790.1380.0580.175
OperatingSystems-0.005-0.0070.0000.0030.0210.0230.0530.022-0.0120.023-0.0031.0000.3750.0270.0800.4650.1180.074
Browser-0.012-0.023-0.020-0.0130.0440.046-0.047-0.0160.0260.021-0.0140.3751.0000.0550.0000.4720.0590.038
Region0.0090.019-0.023-0.015-0.021-0.010-0.018-0.0040.001-0.015-0.0270.0270.0551.000-0.0040.1800.0170.010
TrafficType-0.012-0.015-0.029-0.026-0.070-0.0730.0160.022-0.0180.1100.0790.0800.000-0.0041.0000.3160.0920.121
VisitorType0.0860.0070.0280.0080.0790.0350.1230.1840.1100.0640.1380.4650.4720.1800.3161.0000.0540.104
Weekend0.0280.0000.0110.0000.0000.0040.0500.0650.0310.2590.0580.1180.0590.0170.0920.0541.0000.028
Revenue0.1310.0640.0780.0680.1270.0720.1700.2450.4130.0860.1750.0740.0380.0100.1210.1040.0281.000

Missing values

2023-02-13T16:11:17.217249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T16:11:17.493763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRates in %ExitRates in %PageValuesSpecialDay (probability)MonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.021111200
100.000.0264.0000000.0000000.1000000.00.022212200
200.000.010.0000000.2000000.2000000.00.024193200
300.000.022.6666670.0500000.1400000.00.023224200
400.000.010627.5000000.0200000.0500000.00.023314210
500.000.019154.2166670.0157890.0245610.00.022213200
600.000.010.0000000.2000000.2000000.00.422433200
710.000.000.0000000.2000000.2000000.00.021215210
800.000.0237.0000000.0000000.1000000.00.822223200
900.000.03738.0000000.0000000.0222220.00.422412200
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRates in %ExitRates in %PageValuesSpecialDay (probability)MonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.072231200
1232100.0000.060.0000000.2000000.2000000.0000000.071841200
12322676.2500.0221075.2500000.0000000.0041670.0000000.012242200
12323264.7500.0441157.9761900.0000000.0139530.0000000.0722110200
1232400.0010.016503.0000000.0000000.0376470.0000000.072211200
123253145.0000.0531783.7916670.0071430.02903112.2417170.014611210
1232600.0000.05465.7500000.0000000.0213330.0000000.073218210
1232700.0000.06184.2500000.0833330.0866670.0000000.0732113210
12328475.0000.015346.0000000.0000000.0210530.0000000.0722311200
1232900.0000.0321.2500000.0000000.0666670.0000000.073212010

Duplicate rows

Most frequently occurring

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRates in %ExitRates in %PageValuesSpecialDay (probability)MonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue# duplicates
2600.000.010.00.20.20.00.05221120014
3600.000.010.00.20.20.00.0532312007
4400.000.010.00.20.20.00.0622132007
3800.000.010.00.20.20.00.0611132006
1300.000.010.00.20.20.00.018139201005
3400.000.010.00.20.20.00.0532112004
4100.000.010.00.20.20.00.0611432004
6000.000.010.00.20.20.00.0722112004
000.000.010.00.20.20.00.0111112103
300.000.010.00.20.20.00.0111412103